no code implementations • 22 Apr 2024 • Muhammad Faris, Mario Zanon, Paolo Falcone
In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs).
no code implementations • 18 Dec 2023 • Pablo Krupa, Mario Zanon, Alberto Bemporad
This work presents a nonlinear MPC framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch.
no code implementations • 12 Sep 2023 • Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches as closely as possible the output constraint set, while being included in it.
no code implementations • 5 Dec 2022 • Morten Ryberg Wahlgreen, John Bagterp Jørgensen, Mario Zanon
The PI tuning offers a wide range of tuning possibilities that is then inherited by the MPC design.
no code implementations • 5 Dec 2022 • Morten Wahlgreen Kaysfeld, Mario Zanon, John Bagterp Jørgensen
We perform high-performance Monte Carlo simulations in C enabled by a new thread-safe NMPC implementation in combination with an existing high-performance Monte Carlo simulation toolbox in C. We express the NMPC regulator as an optimal control problem (OCP), which we solve with the new thread-safe sequential quadratic programming software NLPSQP.
no code implementations • 9 Oct 2022 • Arash Bahari Kordabad, Mario Zanon, Sebastien Gros
This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an inexact model.
no code implementations • 19 Nov 2021 • Robert Hult, Mario Zanon, Sebastien Gros, Paolo Falcone
In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders.
no code implementations • 18 Nov 2021 • Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC).
no code implementations • 10 Sep 2021 • Ivo Batkovic, Mohammad Ali, Paolo Falcone, Mario Zanon
Model Predictive Control (MPC) formulations are typically built on the requirement that a feasible reference trajectory is available.
no code implementations • 17 Jun 2021 • Mario Zanon, Sébastien Gros
Economic Model Predictive Control has recently gained popularity due to its ability to directly optimize a given performance criterion, while enforcing constraint satisfaction for nonlinear systems.
no code implementations • 22 Apr 2021 • Sébastien Gros, Mario Zanon
Economic Model Predictive Control (MPC) dissipativity theory is central to discussing the stability of policies resulting from minimizing economic stage costs.
no code implementations • 2 Feb 2021 • Mario Zanon, Sébastien Gros, Michele Palladino
This observation will entail that the MPC-based policy with stability requirements will produce the optimal policy for the discounted MDP if it is stable, and the best stabilizing policy otherwise.
2 code implementations • 29 Jan 2021 • Nicolò Vallarano, Matteo Bruno, Emiliano Marchese, Giuseppe Trapani, Fabio Saracco, Tiziano Squartini, Giulio Cimini, Mario Zanon
Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years.
Data Analysis, Statistics and Probability Statistical Mechanics
no code implementations • 14 Dec 2020 • Sébastien Gros, Mario Zanon
The combination of learning methods with Model Predictive Control (MPC) has attracted a significant amount of attention in the recent literature.
no code implementations • 3 Apr 2020 • Sebastien Gros, Mario Zanon
Model Predictive Control has been recently proposed as policy approximation for Reinforcement Learning, offering a path towards safe and explainable Reinforcement Learning.
no code implementations • 2 Apr 2020 • Sebastien Gros, Mario Zanon, Alberto Bemporad
For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy.
no code implementations • 23 Mar 2020 • Mario Zanon, Alberto Bemporad
When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active.
no code implementations • 30 Jan 2020 • Ivo Batkovic, Mohammad Ali, Paolo Falcone, Mario Zanon
In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility.
no code implementations • 13 Mar 2018 • Ivo Batkovic, Mario Zanon, Nils Lubbe, Paolo Falcone
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.
Systems and Control